{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ee441e7",
   "metadata": {},
   "source": [
    "# RegNet：网络结构设计的新范式 —— 创新点总结\n",
    "\n",
    "**RegNet（Regularized Network）** 是 Facebook AI 提出的一种新型网络结构设计方法，旨在探索更高效、更通用的神经网络架构。它不仅在性能上优于传统的网络（如 ResNet、EfficientNet 等），更重要的是其设计理念带来了架构搜索领域的范式转变。\n",
    "\n",
    "## 1. 从“手工设计”到“设计空间建模”\n",
    "\n",
    "传统网络设计依赖专家经验手动调整层数、通道数等参数，而 RegNet 提出了一种新的思路：将神经网络结构抽象为**设计空间（Design Space）**，并通过统计方法建模和优化这一空间。\n",
    "\n",
    "- **核心思想**：不是设计单个网络，而是设计一个网络结构的“分布”或“族”。\n",
    "- **方法**：使用简单统计模型（如线性回归）分析大量随机网络的性能，找出影响性能的关键因素。\n",
    "\n",
    "## 2. 线性变化的块宽度（Block Width）\n",
    "\n",
    "RegNet 发现，**网络中各阶段（stage）的块宽度（即通道数）应线性增长**，而不是像 ResNet 那样指数增长或固定。\n",
    "\n",
    "- 通过大量实验验证，线性增长的宽度在精度与效率之间取得了更好的平衡。\n",
    "- 这一发现简化了网络设计，也提升了模型的泛化能力。\n",
    "\n",
    "## 3. 统一的网络结构模板\n",
    "\n",
    "RegNet 提出了一种**通用的网络结构模板（Template）**，该模板由以下三个关键参数控制：\n",
    "\n",
    "- **深度（Depth）**：网络的总层数。\n",
    "- **宽度（Width）**：初始通道数及增长方式。\n",
    "- **组宽（Group Width）**：每个卷积块的分组宽度（如使用分组卷积）。\n",
    "\n",
    "这种模板使得 RegNet 可以灵活地适应不同计算预算（FLOPs），生成多个子模型（如 RegNetY、RegNetX 等）。\n",
    "\n",
    "## 4. 简单但高效的结构\n",
    "\n",
    "RegNet 摒弃了复杂的模块设计（如 SE 模块、多分支结构），采用**统一的瓶颈块结构**（Bottleneck Block）和**分组卷积（Grouped Convolution）**，在保持高效的同时提升了性能。\n",
    "\n",
    "## 5. 可扩展性强\n",
    "\n",
    "基于设计空间建模，Facebook 提出了多个 RegNet 变体，包括：\n",
    "\n",
    "- **RegNetX**：使用分组卷积，注重精度与效率平衡。\n",
    "- **RegNetY**：加入 SE 注意力机制，提升精度。\n",
    "\n",
    "## 6. 可解释性强的设计原则\n",
    "\n",
    "RegNet 通过统计建模，总结出了一些**可解释的设计原则**，例如：\n",
    "\n",
    "- 更深的网络不一定更好，深度与宽度需平衡。\n",
    "- 分组卷积的组宽对性能有显著影响，存在最优值。\n",
    "- 宽度线性增长比指数增长更优。\n",
    "\n",
    "这些原则不仅指导了 RegNet 的设计，也为后续网络架构搜索提供了理论依据。\n",
    "\n",
    "---\n",
    "\n",
    "## 总结\n",
    "\n",
    "| 创新点             | 描述                                                         |\n",
    "|--------------------|--------------------------------------------------------------|\n",
    "| 设计空间建模       | 将网络结构设计转化为统计建模问题                             |\n",
    "| 线性宽度增长       | 不同阶段通道数线性增长                                       |\n",
    "| 模块化模板         | 用三个参数统一控制网络结构                                   |\n",
    "| 简洁高效           | 使用简单瓶颈块和分组卷积                                     |\n",
    "| 可扩展性强         | 支持多种变体适应不同场景                                     |\n",
    "| 可解释性强         | 提出可验证、可复用的设计原则                                 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efce5039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e62eda45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1484d6b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------regnet_y_400mf------------------------\n",
      "#Parameter: 4344144\n",
      "Epoch: 1/150 Train Loss: 3.0874 Accuracy: 0.2007 Time: 7.09031  | Val Loss: 2.9240 Accuracy: 0.3302\n",
      "Epoch: 2/150 Train Loss: 2.7402 Accuracy: 0.3397 Time: 6.67413  | Val Loss: 2.6695 Accuracy: 0.4176\n",
      "Epoch: 3/150 Train Loss: 2.5836 Accuracy: 0.4064 Time: 6.71223  | Val Loss: 2.3641 Accuracy: 0.5027\n",
      "Epoch: 4/150 Train Loss: 2.4618 Accuracy: 0.4625 Time: 6.72344  | Val Loss: 2.0725 Accuracy: 0.6176\n",
      "Epoch: 5/150 Train Loss: 2.3652 Accuracy: 0.4918 Time: 6.69325  | Val Loss: 2.0164 Accuracy: 0.6461\n",
      "Epoch: 6/150 Train Loss: 2.3065 Accuracy: 0.5224 Time: 6.64050  | Val Loss: 2.0366 Accuracy: 0.6196\n",
      "Epoch: 7/150 Train Loss: 2.2193 Accuracy: 0.5494 Time: 6.74367  | Val Loss: 2.1001 Accuracy: 0.6079\n",
      "Epoch: 8/150 Train Loss: 2.1652 Accuracy: 0.5821 Time: 6.70320  | Val Loss: 1.8678 Accuracy: 0.7039\n",
      "Epoch: 9/150 Train Loss: 2.1154 Accuracy: 0.5915 Time: 6.67888  | Val Loss: 1.7983 Accuracy: 0.7241\n",
      "Epoch: 10/150 Train Loss: 2.0548 Accuracy: 0.6182 Time: 6.82825  | Val Loss: 1.8641 Accuracy: 0.6922\n",
      "Epoch: 11/150 Train Loss: 2.0060 Accuracy: 0.6373 Time: 6.74117  | Val Loss: 1.8085 Accuracy: 0.7113\n",
      "Epoch: 12/150 Train Loss: 1.9967 Accuracy: 0.6426 Time: 6.69121  | Val Loss: 1.8217 Accuracy: 0.7192\n",
      "Epoch: 13/150 Train Loss: 1.9329 Accuracy: 0.6684 Time: 6.73387  | Val Loss: 1.7018 Accuracy: 0.7618\n",
      "Epoch: 14/150 Train Loss: 1.9050 Accuracy: 0.6818 Time: 6.72959  | Val Loss: 1.6881 Accuracy: 0.7610\n",
      "Epoch: 15/150 Train Loss: 1.8583 Accuracy: 0.6943 Time: 6.77936  | Val Loss: 1.6865 Accuracy: 0.7656\n",
      "Epoch: 16/150 Train Loss: 1.8339 Accuracy: 0.7031 Time: 6.73429  | Val Loss: 1.6326 Accuracy: 0.7819\n",
      "Epoch: 17/150 Train Loss: 1.8055 Accuracy: 0.7122 Time: 6.75577  | Val Loss: 1.6028 Accuracy: 0.7980\n",
      "Epoch: 18/150 Train Loss: 1.7972 Accuracy: 0.7155 Time: 6.74482  | Val Loss: 1.6239 Accuracy: 0.7827\n",
      "Epoch: 19/150 Train Loss: 1.7520 Accuracy: 0.7327 Time: 6.77489  | Val Loss: 1.5646 Accuracy: 0.8013\n",
      "Epoch: 20/150 Train Loss: 1.7401 Accuracy: 0.7368 Time: 6.90036  | Val Loss: 1.5910 Accuracy: 0.7972\n",
      "Epoch: 21/150 Train Loss: 1.7273 Accuracy: 0.7430 Time: 6.69810  | Val Loss: 1.5307 Accuracy: 0.8171\n",
      "Epoch: 22/150 Train Loss: 1.7006 Accuracy: 0.7487 Time: 6.78447  | Val Loss: 1.5208 Accuracy: 0.8250\n",
      "Epoch: 23/150 Train Loss: 1.6873 Accuracy: 0.7553 Time: 6.83550  | Val Loss: 1.5264 Accuracy: 0.8239\n",
      "Epoch: 24/150 Train Loss: 1.6541 Accuracy: 0.7729 Time: 6.83714  | Val Loss: 1.5400 Accuracy: 0.8120\n",
      "Epoch: 25/150 Train Loss: 1.6506 Accuracy: 0.7670 Time: 6.80675  | Val Loss: 1.4829 Accuracy: 0.8354\n",
      "Epoch: 26/150 Train Loss: 1.6352 Accuracy: 0.7769 Time: 6.77566  | Val Loss: 1.4895 Accuracy: 0.8288\n",
      "Epoch: 27/150 Train Loss: 1.6207 Accuracy: 0.7829 Time: 6.76710  | Val Loss: 1.4300 Accuracy: 0.8543\n",
      "Epoch: 28/150 Train Loss: 1.5959 Accuracy: 0.7944 Time: 6.70578  | Val Loss: 1.4676 Accuracy: 0.8405\n",
      "Epoch: 29/150 Train Loss: 1.5782 Accuracy: 0.7929 Time: 6.79453  | Val Loss: 1.4552 Accuracy: 0.8469\n",
      "Epoch: 30/150 Train Loss: 1.5744 Accuracy: 0.7956 Time: 6.74322  | Val Loss: 1.4335 Accuracy: 0.8522\n",
      "Epoch: 31/150 Train Loss: 1.5719 Accuracy: 0.7987 Time: 6.82422  | Val Loss: 1.4335 Accuracy: 0.8550\n",
      "Epoch: 32/150 Train Loss: 1.5373 Accuracy: 0.8123 Time: 6.78260  | Val Loss: 1.4160 Accuracy: 0.8617\n",
      "Epoch: 33/150 Train Loss: 1.5269 Accuracy: 0.8184 Time: 6.71244  | Val Loss: 1.4729 Accuracy: 0.8418\n",
      "Epoch: 34/150 Train Loss: 1.5319 Accuracy: 0.8129 Time: 6.72800  | Val Loss: 1.3967 Accuracy: 0.8673\n",
      "Epoch: 35/150 Train Loss: 1.5329 Accuracy: 0.8122 Time: 6.80825  | Val Loss: 1.4133 Accuracy: 0.8594\n",
      "Epoch: 36/150 Train Loss: 1.5098 Accuracy: 0.8228 Time: 6.74986  | Val Loss: 1.4060 Accuracy: 0.8647\n",
      "Epoch: 37/150 Train Loss: 1.5064 Accuracy: 0.8242 Time: 6.78992  | Val Loss: 1.4280 Accuracy: 0.8522\n",
      "Epoch: 38/150 Train Loss: 1.5077 Accuracy: 0.8197 Time: 6.68171  | Val Loss: 1.4223 Accuracy: 0.8591\n",
      "Epoch: 39/150 Train Loss: 1.4771 Accuracy: 0.8312 Time: 6.68485  | Val Loss: 1.4343 Accuracy: 0.8586\n",
      "Epoch: 40/150 Train Loss: 1.4852 Accuracy: 0.8355 Time: 6.78270  | Val Loss: 1.4550 Accuracy: 0.8448\n",
      "Epoch: 41/150 Train Loss: 1.4533 Accuracy: 0.8440 Time: 6.68724  | Val Loss: 1.3642 Accuracy: 0.8744\n",
      "Epoch: 42/150 Train Loss: 1.4555 Accuracy: 0.8398 Time: 6.71608  | Val Loss: 1.3874 Accuracy: 0.8711\n",
      "Epoch: 43/150 Train Loss: 1.4404 Accuracy: 0.8466 Time: 6.72915  | Val Loss: 1.3591 Accuracy: 0.8787\n",
      "Epoch: 44/150 Train Loss: 1.4420 Accuracy: 0.8472 Time: 6.67693  | Val Loss: 1.3736 Accuracy: 0.8734\n",
      "Epoch: 45/150 Train Loss: 1.4192 Accuracy: 0.8553 Time: 6.71985  | Val Loss: 1.3614 Accuracy: 0.8797\n",
      "Epoch: 46/150 Train Loss: 1.4253 Accuracy: 0.8525 Time: 6.68974  | Val Loss: 1.3590 Accuracy: 0.8741\n",
      "Epoch: 47/150 Train Loss: 1.4216 Accuracy: 0.8497 Time: 6.76162  | Val Loss: 1.3470 Accuracy: 0.8828\n",
      "Epoch: 48/150 Train Loss: 1.4218 Accuracy: 0.8518 Time: 6.66745  | Val Loss: 1.3686 Accuracy: 0.8815\n",
      "Epoch: 49/150 Train Loss: 1.3962 Accuracy: 0.8663 Time: 6.71630  | Val Loss: 1.3387 Accuracy: 0.8861\n",
      "Epoch: 50/150 Train Loss: 1.3966 Accuracy: 0.8652 Time: 6.67107  | Val Loss: 1.3568 Accuracy: 0.8759\n",
      "Epoch: 51/150 Train Loss: 1.3927 Accuracy: 0.8602 Time: 6.64279  | Val Loss: 1.3329 Accuracy: 0.8904\n",
      "Epoch: 52/150 Train Loss: 1.3906 Accuracy: 0.8607 Time: 6.63756  | Val Loss: 1.3336 Accuracy: 0.8892\n",
      "Epoch: 53/150 Train Loss: 1.3793 Accuracy: 0.8695 Time: 6.73848  | Val Loss: 1.3444 Accuracy: 0.8838\n",
      "Epoch: 54/150 Train Loss: 1.3663 Accuracy: 0.8705 Time: 6.63377  | Val Loss: 1.3681 Accuracy: 0.8803\n",
      "Epoch: 55/150 Train Loss: 1.3724 Accuracy: 0.8700 Time: 6.65571  | Val Loss: 1.3151 Accuracy: 0.8915\n",
      "Epoch: 56/150 Train Loss: 1.3662 Accuracy: 0.8709 Time: 6.73890  | Val Loss: 1.3005 Accuracy: 0.8966\n",
      "Epoch: 57/150 Train Loss: 1.3564 Accuracy: 0.8795 Time: 6.67602  | Val Loss: 1.3130 Accuracy: 0.8986\n",
      "Epoch: 58/150 Train Loss: 1.3432 Accuracy: 0.8796 Time: 6.64601  | Val Loss: 1.3097 Accuracy: 0.9001\n",
      "Epoch: 59/150 Train Loss: 1.3467 Accuracy: 0.8802 Time: 6.69827  | Val Loss: 1.2995 Accuracy: 0.9034\n",
      "Epoch: 60/150 Train Loss: 1.3354 Accuracy: 0.8862 Time: 6.76320  | Val Loss: 1.3498 Accuracy: 0.8831\n",
      "Epoch: 61/150 Train Loss: 1.3453 Accuracy: 0.8802 Time: 6.62754  | Val Loss: 1.2982 Accuracy: 0.9022\n",
      "Epoch: 62/150 Train Loss: 1.3266 Accuracy: 0.8916 Time: 6.73580  | Val Loss: 1.3198 Accuracy: 0.8938\n",
      "Epoch: 63/150 Train Loss: 1.3141 Accuracy: 0.8935 Time: 6.80344  | Val Loss: 1.3216 Accuracy: 0.8968\n",
      "Epoch: 64/150 Train Loss: 1.3138 Accuracy: 0.8919 Time: 6.71622  | Val Loss: 1.3090 Accuracy: 0.8991\n",
      "Epoch: 65/150 Train Loss: 1.3107 Accuracy: 0.8968 Time: 6.74321  | Val Loss: 1.3023 Accuracy: 0.9029\n",
      "Epoch: 66/150 Train Loss: 1.3074 Accuracy: 0.8940 Time: 6.62277  | Val Loss: 1.3135 Accuracy: 0.8978\n",
      "Epoch: 67/150 Train Loss: 1.2984 Accuracy: 0.8967 Time: 6.68630  | Val Loss: 1.2856 Accuracy: 0.9060\n",
      "Epoch: 68/150 Train Loss: 1.2881 Accuracy: 0.9043 Time: 6.72456  | Val Loss: 1.3013 Accuracy: 0.9019\n",
      "Epoch: 69/150 Train Loss: 1.2889 Accuracy: 0.8987 Time: 6.72258  | Val Loss: 1.2925 Accuracy: 0.9078\n",
      "Epoch: 70/150 Train Loss: 1.2928 Accuracy: 0.8989 Time: 6.75452  | Val Loss: 1.2913 Accuracy: 0.8994\n",
      "Epoch: 71/150 Train Loss: 1.2650 Accuracy: 0.9114 Time: 6.74346  | Val Loss: 1.2805 Accuracy: 0.9118\n",
      "Epoch: 72/150 Train Loss: 1.2774 Accuracy: 0.9057 Time: 6.76855  | Val Loss: 1.3049 Accuracy: 0.9001\n",
      "Epoch: 73/150 Train Loss: 1.2659 Accuracy: 0.9108 Time: 6.79369  | Val Loss: 1.2877 Accuracy: 0.9029\n",
      "Epoch: 74/150 Train Loss: 1.2711 Accuracy: 0.9085 Time: 6.76879  | Val Loss: 1.2748 Accuracy: 0.9096\n",
      "Epoch: 75/150 Train Loss: 1.2622 Accuracy: 0.9136 Time: 6.86212  | Val Loss: 1.2844 Accuracy: 0.9090\n",
      "Epoch: 76/150 Train Loss: 1.2666 Accuracy: 0.9090 Time: 6.79846  | Val Loss: 1.2761 Accuracy: 0.9162\n",
      "Epoch: 77/150 Train Loss: 1.2516 Accuracy: 0.9147 Time: 6.73827  | Val Loss: 1.2725 Accuracy: 0.9169\n",
      "Epoch: 78/150 Train Loss: 1.2505 Accuracy: 0.9147 Time: 6.73241  | Val Loss: 1.2703 Accuracy: 0.9180\n",
      "Epoch: 79/150 Train Loss: 1.2415 Accuracy: 0.9183 Time: 6.76904  | Val Loss: 1.2808 Accuracy: 0.9131\n",
      "Epoch: 80/150 Train Loss: 1.2352 Accuracy: 0.9216 Time: 6.89562  | Val Loss: 1.2864 Accuracy: 0.9108\n",
      "Epoch: 81/150 Train Loss: 1.2290 Accuracy: 0.9238 Time: 6.76621  | Val Loss: 1.2903 Accuracy: 0.9131\n",
      "Epoch: 82/150 Train Loss: 1.2301 Accuracy: 0.9254 Time: 6.83216  | Val Loss: 1.3075 Accuracy: 0.9078\n",
      "Epoch: 83/150 Train Loss: 1.2255 Accuracy: 0.9229 Time: 6.76930  | Val Loss: 1.2767 Accuracy: 0.9208\n",
      "Epoch: 84/150 Train Loss: 1.2258 Accuracy: 0.9248 Time: 6.81365  | Val Loss: 1.2880 Accuracy: 0.9131\n",
      "Epoch: 85/150 Train Loss: 1.2170 Accuracy: 0.9264 Time: 6.69689  | Val Loss: 1.2972 Accuracy: 0.9085\n",
      "Epoch: 86/150 Train Loss: 1.2195 Accuracy: 0.9248 Time: 6.82397  | Val Loss: 1.2622 Accuracy: 0.9172\n",
      "Epoch: 87/150 Train Loss: 1.2135 Accuracy: 0.9301 Time: 6.78273  | Val Loss: 1.2866 Accuracy: 0.9096\n",
      "Epoch: 88/150 Train Loss: 1.2007 Accuracy: 0.9332 Time: 6.87076  | Val Loss: 1.2830 Accuracy: 0.9141\n",
      "Epoch: 89/150 Train Loss: 1.2102 Accuracy: 0.9297 Time: 6.79071  | Val Loss: 1.2763 Accuracy: 0.9141\n",
      "Epoch: 90/150 Train Loss: 1.1959 Accuracy: 0.9355 Time: 6.79016  | Val Loss: 1.2753 Accuracy: 0.9157\n",
      "Epoch: 91/150 Train Loss: 1.2050 Accuracy: 0.9353 Time: 6.74057  | Val Loss: 1.2794 Accuracy: 0.9126\n",
      "Epoch: 92/150 Train Loss: 1.1882 Accuracy: 0.9375 Time: 6.78332  | Val Loss: 1.2752 Accuracy: 0.9152\n",
      "Epoch: 93/150 Train Loss: 1.1899 Accuracy: 0.9366 Time: 6.76843  | Val Loss: 1.2775 Accuracy: 0.9192\n",
      "Epoch: 94/150 Train Loss: 1.1915 Accuracy: 0.9361 Time: 6.77588  | Val Loss: 1.2772 Accuracy: 0.9157\n",
      "Epoch: 95/150 Train Loss: 1.1960 Accuracy: 0.9366 Time: 6.78737  | Val Loss: 1.2815 Accuracy: 0.9159\n",
      "Epoch: 96/150 Train Loss: 1.1760 Accuracy: 0.9439 Time: 6.78942  | Val Loss: 1.2753 Accuracy: 0.9169\n",
      "Epoch: 97/150 Train Loss: 1.1872 Accuracy: 0.9404 Time: 6.71959  | Val Loss: 1.2902 Accuracy: 0.9154\n",
      "Epoch: 98/150 Train Loss: 1.1729 Accuracy: 0.9455 Time: 6.73261  | Val Loss: 1.2814 Accuracy: 0.9175\n",
      "Epoch: 99/150 Train Loss: 1.1677 Accuracy: 0.9451 Time: 6.73057  | Val Loss: 1.2807 Accuracy: 0.9172\n",
      "Epoch: 100/150 Train Loss: 1.1693 Accuracy: 0.9461 Time: 6.88514  | Val Loss: 1.2827 Accuracy: 0.9159\n",
      "Epoch: 101/150 Train Loss: 1.1576 Accuracy: 0.9499 Time: 6.74648  | Val Loss: 1.2731 Accuracy: 0.9210\n",
      "Epoch: 102/150 Train Loss: 1.1743 Accuracy: 0.9410 Time: 6.79787  | Val Loss: 1.2769 Accuracy: 0.9228\n",
      "Epoch: 103/150 Train Loss: 1.1672 Accuracy: 0.9453 Time: 6.87725  | Val Loss: 1.2660 Accuracy: 0.9228\n",
      "Epoch: 104/150 Train Loss: 1.1622 Accuracy: 0.9483 Time: 6.72084  | Val Loss: 1.2685 Accuracy: 0.9208\n",
      "Epoch: 105/150 Train Loss: 1.1679 Accuracy: 0.9440 Time: 6.72085  | Val Loss: 1.2653 Accuracy: 0.9243\n",
      "Epoch: 106/150 Train Loss: 1.1487 Accuracy: 0.9538 Time: 6.64289  | Val Loss: 1.2711 Accuracy: 0.9218\n",
      "Epoch: 107/150 Train Loss: 1.1506 Accuracy: 0.9527 Time: 6.65519  | Val Loss: 1.2613 Accuracy: 0.9259\n",
      "Epoch: 108/150 Train Loss: 1.1567 Accuracy: 0.9497 Time: 6.70245  | Val Loss: 1.2702 Accuracy: 0.9225\n",
      "Epoch: 109/150 Train Loss: 1.1488 Accuracy: 0.9521 Time: 6.68492  | Val Loss: 1.2735 Accuracy: 0.9220\n",
      "Epoch: 110/150 Train Loss: 1.1481 Accuracy: 0.9525 Time: 6.69189  | Val Loss: 1.2708 Accuracy: 0.9233\n",
      "Epoch: 111/150 Train Loss: 1.1414 Accuracy: 0.9544 Time: 6.65005  | Val Loss: 1.2650 Accuracy: 0.9220\n",
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      "Epoch: 122/150 Train Loss: 1.1321 Accuracy: 0.9574 Time: 6.64663  | Val Loss: 1.2591 Accuracy: 0.9279\n",
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      "Epoch: 129/150 Train Loss: 1.1177 Accuracy: 0.9648 Time: 6.65550  | Val Loss: 1.2672 Accuracy: 0.9256\n",
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      "Epoch: 132/150 Train Loss: 1.1104 Accuracy: 0.9674 Time: 6.67630  | Val Loss: 1.2673 Accuracy: 0.9259\n",
      "Epoch: 133/150 Train Loss: 1.1159 Accuracy: 0.9624 Time: 6.70396  | Val Loss: 1.2677 Accuracy: 0.9264\n",
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      "Epoch: 135/150 Train Loss: 1.1127 Accuracy: 0.9682 Time: 6.66506  | Val Loss: 1.2684 Accuracy: 0.9274\n",
      "Epoch: 136/150 Train Loss: 1.1171 Accuracy: 0.9626 Time: 6.66080  | Val Loss: 1.2643 Accuracy: 0.9259\n",
      "Epoch: 137/150 Train Loss: 1.1157 Accuracy: 0.9646 Time: 6.73527  | Val Loss: 1.2610 Accuracy: 0.9287\n",
      "Epoch: 138/150 Train Loss: 1.1087 Accuracy: 0.9681 Time: 6.66616  | Val Loss: 1.2676 Accuracy: 0.9251\n",
      "Epoch: 139/150 Train Loss: 1.1163 Accuracy: 0.9644 Time: 6.68871  | Val Loss: 1.2698 Accuracy: 0.9241\n",
      "Epoch: 140/150 Train Loss: 1.1146 Accuracy: 0.9642 Time: 6.65288  | Val Loss: 1.2648 Accuracy: 0.9274\n",
      "Epoch: 141/150 Train Loss: 1.1123 Accuracy: 0.9682 Time: 6.68065  | Val Loss: 1.2648 Accuracy: 0.9276\n",
      "Epoch: 142/150 Train Loss: 1.1121 Accuracy: 0.9671 Time: 6.67718  | Val Loss: 1.2665 Accuracy: 0.9274\n",
      "Epoch: 143/150 Train Loss: 1.1142 Accuracy: 0.9663 Time: 6.71547  | Val Loss: 1.2651 Accuracy: 0.9279\n",
      "Epoch: 144/150 Train Loss: 1.1133 Accuracy: 0.9664 Time: 6.66677  | Val Loss: 1.2657 Accuracy: 0.9274\n",
      "Epoch: 145/150 Train Loss: 1.1086 Accuracy: 0.9676 Time: 6.62816  | Val Loss: 1.2621 Accuracy: 0.9294\n",
      "Epoch: 146/150 Train Loss: 1.1097 Accuracy: 0.9662 Time: 6.65349  | Val Loss: 1.2677 Accuracy: 0.9294\n",
      "Epoch: 147/150 Train Loss: 1.1156 Accuracy: 0.9657 Time: 6.68554  | Val Loss: 1.2660 Accuracy: 0.9279\n",
      "Epoch: 148/150 Train Loss: 1.1064 Accuracy: 0.9693 Time: 6.62026  | Val Loss: 1.2653 Accuracy: 0.9297\n",
      "Epoch: 149/150 Train Loss: 1.1099 Accuracy: 0.9668 Time: 6.69679  | Val Loss: 1.2639 Accuracy: 0.9304\n",
      "Epoch: 150/150 Train Loss: 1.1092 Accuracy: 0.9655 Time: 6.65871  | Val Loss: 1.2640 Accuracy: 0.9304\n",
      "-----------------------regnet_y_800mf------------------------\n",
      "#Parameter: 6432512\n",
      "Epoch: 1/150 Train Loss: 2.9879 Accuracy: 0.2275 Time: 7.68605  | Val Loss: 2.7751 Accuracy: 0.3350\n",
      "Epoch: 2/150 Train Loss: 2.6890 Accuracy: 0.3568 Time: 7.62486  | Val Loss: 2.3494 Accuracy: 0.5103\n",
      "Epoch: 3/150 Train Loss: 2.5144 Accuracy: 0.4363 Time: 7.73339  | Val Loss: 2.2054 Accuracy: 0.5643\n",
      "Epoch: 4/150 Train Loss: 2.4042 Accuracy: 0.4837 Time: 7.87711  | Val Loss: 2.0273 Accuracy: 0.6385\n",
      "Epoch: 5/150 Train Loss: 2.2734 Accuracy: 0.5305 Time: 7.78367  | Val Loss: 1.9403 Accuracy: 0.6708\n",
      "Epoch: 6/150 Train Loss: 2.1846 Accuracy: 0.5708 Time: 7.96375  | Val Loss: 1.8717 Accuracy: 0.6996\n",
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      "Epoch: 8/150 Train Loss: 2.0594 Accuracy: 0.6190 Time: 7.62024  | Val Loss: 1.7406 Accuracy: 0.7455\n",
      "Epoch: 9/150 Train Loss: 2.0051 Accuracy: 0.6410 Time: 7.74964  | Val Loss: 1.7364 Accuracy: 0.7452\n",
      "Epoch: 10/150 Train Loss: 1.9416 Accuracy: 0.6705 Time: 7.98304  | Val Loss: 1.6440 Accuracy: 0.7761\n",
      "Epoch: 11/150 Train Loss: 1.8980 Accuracy: 0.6783 Time: 7.69055  | Val Loss: 1.6247 Accuracy: 0.7855\n",
      "Epoch: 12/150 Train Loss: 1.8679 Accuracy: 0.6948 Time: 7.93626  | Val Loss: 1.6171 Accuracy: 0.7786\n",
      "Epoch: 13/150 Train Loss: 1.8324 Accuracy: 0.7056 Time: 7.83296  | Val Loss: 1.5547 Accuracy: 0.8158\n",
      "Epoch: 14/150 Train Loss: 1.7885 Accuracy: 0.7232 Time: 7.70518  | Val Loss: 1.6132 Accuracy: 0.7944\n",
      "Epoch: 15/150 Train Loss: 1.7470 Accuracy: 0.7376 Time: 7.85082  | Val Loss: 1.6307 Accuracy: 0.7750\n",
      "Epoch: 16/150 Train Loss: 1.7301 Accuracy: 0.7406 Time: 7.80192  | Val Loss: 1.5227 Accuracy: 0.8161\n",
      "Epoch: 17/150 Train Loss: 1.7109 Accuracy: 0.7518 Time: 7.72478  | Val Loss: 1.5207 Accuracy: 0.8211\n",
      "Epoch: 18/150 Train Loss: 1.6960 Accuracy: 0.7480 Time: 7.79643  | Val Loss: 1.5037 Accuracy: 0.8318\n",
      "Epoch: 19/150 Train Loss: 1.6665 Accuracy: 0.7622 Time: 7.77098  | Val Loss: 1.4656 Accuracy: 0.8375\n",
      "Epoch: 20/150 Train Loss: 1.6325 Accuracy: 0.7817 Time: 8.00901  | Val Loss: 1.4383 Accuracy: 0.8530\n",
      "Epoch: 21/150 Train Loss: 1.6316 Accuracy: 0.7734 Time: 7.96020  | Val Loss: 1.4434 Accuracy: 0.8456\n",
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      "Epoch: 28/150 Train Loss: 1.5241 Accuracy: 0.8179 Time: 7.66738  | Val Loss: 1.3870 Accuracy: 0.8724\n",
      "Epoch: 29/150 Train Loss: 1.5269 Accuracy: 0.8167 Time: 7.81743  | Val Loss: 1.3889 Accuracy: 0.8752\n",
      "Epoch: 30/150 Train Loss: 1.4967 Accuracy: 0.8270 Time: 7.68795  | Val Loss: 1.3843 Accuracy: 0.8718\n",
      "Epoch: 31/150 Train Loss: 1.4944 Accuracy: 0.8264 Time: 7.72771  | Val Loss: 1.3678 Accuracy: 0.8833\n",
      "Epoch: 32/150 Train Loss: 1.4825 Accuracy: 0.8303 Time: 8.04471  | Val Loss: 1.3512 Accuracy: 0.8836\n",
      "Epoch: 33/150 Train Loss: 1.4921 Accuracy: 0.8295 Time: 7.96470  | Val Loss: 1.3712 Accuracy: 0.8752\n",
      "Epoch: 34/150 Train Loss: 1.4678 Accuracy: 0.8418 Time: 7.74039  | Val Loss: 1.3573 Accuracy: 0.8785\n",
      "Epoch: 35/150 Train Loss: 1.4630 Accuracy: 0.8406 Time: 7.59853  | Val Loss: 1.3363 Accuracy: 0.8902\n",
      "Epoch: 36/150 Train Loss: 1.4535 Accuracy: 0.8420 Time: 7.85713  | Val Loss: 1.3309 Accuracy: 0.8902\n",
      "Epoch: 37/150 Train Loss: 1.4451 Accuracy: 0.8467 Time: 7.70356  | Val Loss: 1.3615 Accuracy: 0.8775\n",
      "Epoch: 38/150 Train Loss: 1.4389 Accuracy: 0.8453 Time: 7.71793  | Val Loss: 1.3668 Accuracy: 0.8785\n",
      "Epoch: 39/150 Train Loss: 1.4220 Accuracy: 0.8551 Time: 7.66934  | Val Loss: 1.3347 Accuracy: 0.8894\n",
      "Epoch: 40/150 Train Loss: 1.4261 Accuracy: 0.8526 Time: 7.66395  | Val Loss: 1.3111 Accuracy: 0.8991\n",
      "Epoch: 41/150 Train Loss: 1.4153 Accuracy: 0.8576 Time: 7.96012  | Val Loss: 1.3056 Accuracy: 0.8945\n",
      "Epoch: 42/150 Train Loss: 1.4050 Accuracy: 0.8589 Time: 7.79254  | Val Loss: 1.3198 Accuracy: 0.8948\n",
      "Epoch: 43/150 Train Loss: 1.3894 Accuracy: 0.8657 Time: 7.92401  | Val Loss: 1.3139 Accuracy: 0.9006\n",
      "Epoch: 44/150 Train Loss: 1.4052 Accuracy: 0.8612 Time: 7.98164  | Val Loss: 1.3177 Accuracy: 0.8963\n",
      "Epoch: 45/150 Train Loss: 1.3921 Accuracy: 0.8626 Time: 7.70264  | Val Loss: 1.3027 Accuracy: 0.9037\n",
      "Epoch: 46/150 Train Loss: 1.3732 Accuracy: 0.8714 Time: 7.65533  | Val Loss: 1.3185 Accuracy: 0.8917\n",
      "Epoch: 47/150 Train Loss: 1.3712 Accuracy: 0.8736 Time: 7.78499  | Val Loss: 1.3063 Accuracy: 0.8978\n",
      "Epoch: 48/150 Train Loss: 1.3610 Accuracy: 0.8754 Time: 7.85044  | Val Loss: 1.3228 Accuracy: 0.8971\n",
      "Epoch: 49/150 Train Loss: 1.3566 Accuracy: 0.8760 Time: 7.72802  | Val Loss: 1.2806 Accuracy: 0.9141\n",
      "Epoch: 50/150 Train Loss: 1.3450 Accuracy: 0.8796 Time: 7.71808  | Val Loss: 1.2913 Accuracy: 0.9057\n",
      "Epoch: 51/150 Train Loss: 1.3501 Accuracy: 0.8817 Time: 7.74270  | Val Loss: 1.3150 Accuracy: 0.9011\n",
      "Epoch: 52/150 Train Loss: 1.3416 Accuracy: 0.8809 Time: 7.68697  | Val Loss: 1.3135 Accuracy: 0.9024\n",
      "Epoch: 53/150 Train Loss: 1.3414 Accuracy: 0.8793 Time: 7.83652  | Val Loss: 1.2686 Accuracy: 0.9185\n",
      "Epoch: 54/150 Train Loss: 1.3284 Accuracy: 0.8902 Time: 7.64294  | Val Loss: 1.2818 Accuracy: 0.9060\n",
      "Epoch: 55/150 Train Loss: 1.3292 Accuracy: 0.8874 Time: 7.64013  | Val Loss: 1.2762 Accuracy: 0.9070\n",
      "Epoch: 56/150 Train Loss: 1.3250 Accuracy: 0.8869 Time: 7.81571  | Val Loss: 1.2917 Accuracy: 0.9093\n",
      "Epoch: 57/150 Train Loss: 1.3180 Accuracy: 0.8927 Time: 7.64841  | Val Loss: 1.2706 Accuracy: 0.9159\n",
      "Epoch: 58/150 Train Loss: 1.3091 Accuracy: 0.8962 Time: 7.63818  | Val Loss: 1.2808 Accuracy: 0.9101\n",
      "Epoch: 59/150 Train Loss: 1.2949 Accuracy: 0.9009 Time: 7.61058  | Val Loss: 1.2612 Accuracy: 0.9175\n",
      "Epoch: 60/150 Train Loss: 1.2883 Accuracy: 0.9038 Time: 7.74621  | Val Loss: 1.2679 Accuracy: 0.9121\n",
      "Epoch: 61/150 Train Loss: 1.3050 Accuracy: 0.8978 Time: 7.66539  | Val Loss: 1.2725 Accuracy: 0.9144\n",
      "Epoch: 62/150 Train Loss: 1.2854 Accuracy: 0.9027 Time: 7.71696  | Val Loss: 1.2871 Accuracy: 0.9098\n",
      "Epoch: 63/150 Train Loss: 1.2835 Accuracy: 0.9024 Time: 7.68561  | Val Loss: 1.2628 Accuracy: 0.9126\n",
      "Epoch: 64/150 Train Loss: 1.2847 Accuracy: 0.9015 Time: 7.59442  | Val Loss: 1.2660 Accuracy: 0.9159\n",
      "Epoch: 65/150 Train Loss: 1.2714 Accuracy: 0.9085 Time: 7.88502  | Val Loss: 1.2734 Accuracy: 0.9116\n",
      "Epoch: 66/150 Train Loss: 1.2552 Accuracy: 0.9131 Time: 7.61192  | Val Loss: 1.3232 Accuracy: 0.8971\n",
      "Epoch: 67/150 Train Loss: 1.2588 Accuracy: 0.9132 Time: 7.58494  | Val Loss: 1.2599 Accuracy: 0.9180\n",
      "Epoch: 68/150 Train Loss: 1.2626 Accuracy: 0.9102 Time: 7.63285  | Val Loss: 1.2468 Accuracy: 0.9223\n",
      "Epoch: 69/150 Train Loss: 1.2595 Accuracy: 0.9167 Time: 7.82647  | Val Loss: 1.2656 Accuracy: 0.9167\n",
      "Epoch: 70/150 Train Loss: 1.2516 Accuracy: 0.9169 Time: 7.80523  | Val Loss: 1.2707 Accuracy: 0.9159\n",
      "Epoch: 71/150 Train Loss: 1.2509 Accuracy: 0.9179 Time: 7.60676  | Val Loss: 1.2631 Accuracy: 0.9152\n",
      "Epoch: 72/150 Train Loss: 1.2457 Accuracy: 0.9149 Time: 7.61533  | Val Loss: 1.2663 Accuracy: 0.9185\n",
      "Epoch: 73/150 Train Loss: 1.2474 Accuracy: 0.9165 Time: 7.66230  | Val Loss: 1.2791 Accuracy: 0.9113\n",
      "Epoch: 74/150 Train Loss: 1.2328 Accuracy: 0.9201 Time: 7.70495  | Val Loss: 1.2738 Accuracy: 0.9141\n",
      "Epoch: 75/150 Train Loss: 1.2356 Accuracy: 0.9267 Time: 7.62609  | Val Loss: 1.2673 Accuracy: 0.9159\n",
      "Epoch: 76/150 Train Loss: 1.2252 Accuracy: 0.9236 Time: 7.74410  | Val Loss: 1.2854 Accuracy: 0.9101\n",
      "Epoch: 77/150 Train Loss: 1.2322 Accuracy: 0.9212 Time: 7.59793  | Val Loss: 1.2629 Accuracy: 0.9218\n",
      "Epoch: 78/150 Train Loss: 1.2157 Accuracy: 0.9287 Time: 7.65486  | Val Loss: 1.2650 Accuracy: 0.9182\n",
      "Epoch: 79/150 Train Loss: 1.2119 Accuracy: 0.9298 Time: 7.66874  | Val Loss: 1.2671 Accuracy: 0.9159\n",
      "Epoch: 80/150 Train Loss: 1.2065 Accuracy: 0.9341 Time: 7.65931  | Val Loss: 1.2540 Accuracy: 0.9233\n",
      "Epoch: 81/150 Train Loss: 1.2170 Accuracy: 0.9263 Time: 7.64059  | Val Loss: 1.2653 Accuracy: 0.9146\n",
      "Epoch: 82/150 Train Loss: 1.2090 Accuracy: 0.9302 Time: 7.87580  | Val Loss: 1.2437 Accuracy: 0.9220\n",
      "Epoch: 83/150 Train Loss: 1.2058 Accuracy: 0.9343 Time: 7.63950  | Val Loss: 1.2486 Accuracy: 0.9256\n",
      "Epoch: 84/150 Train Loss: 1.1887 Accuracy: 0.9393 Time: 7.73795  | Val Loss: 1.2455 Accuracy: 0.9238\n",
      "Epoch: 85/150 Train Loss: 1.2013 Accuracy: 0.9306 Time: 7.69548  | Val Loss: 1.2623 Accuracy: 0.9180\n",
      "Epoch: 86/150 Train Loss: 1.1861 Accuracy: 0.9381 Time: 7.65710  | Val Loss: 1.2625 Accuracy: 0.9185\n",
      "Epoch: 87/150 Train Loss: 1.1860 Accuracy: 0.9393 Time: 7.55671  | Val Loss: 1.2498 Accuracy: 0.9256\n",
      "Epoch: 88/150 Train Loss: 1.1843 Accuracy: 0.9395 Time: 7.74390  | Val Loss: 1.2573 Accuracy: 0.9233\n",
      "Epoch: 89/150 Train Loss: 1.1909 Accuracy: 0.9364 Time: 7.64830  | Val Loss: 1.2587 Accuracy: 0.9261\n",
      "Epoch: 90/150 Train Loss: 1.1784 Accuracy: 0.9431 Time: 7.64366  | Val Loss: 1.2709 Accuracy: 0.9157\n",
      "Epoch: 91/150 Train Loss: 1.1690 Accuracy: 0.9472 Time: 7.78851  | Val Loss: 1.2630 Accuracy: 0.9197\n",
      "Epoch: 92/150 Train Loss: 1.1707 Accuracy: 0.9436 Time: 7.63225  | Val Loss: 1.2720 Accuracy: 0.9177\n",
      "Epoch: 93/150 Train Loss: 1.1744 Accuracy: 0.9459 Time: 7.64558  | Val Loss: 1.2588 Accuracy: 0.9220\n",
      "Epoch: 94/150 Train Loss: 1.1690 Accuracy: 0.9464 Time: 7.58398  | Val Loss: 1.2625 Accuracy: 0.9187\n",
      "Epoch: 95/150 Train Loss: 1.1645 Accuracy: 0.9479 Time: 7.65333  | Val Loss: 1.2447 Accuracy: 0.9269\n",
      "Epoch: 96/150 Train Loss: 1.1659 Accuracy: 0.9462 Time: 7.82888  | Val Loss: 1.2645 Accuracy: 0.9200\n",
      "Epoch: 97/150 Train Loss: 1.1639 Accuracy: 0.9476 Time: 7.67603  | Val Loss: 1.2394 Accuracy: 0.9315\n",
      "Epoch: 98/150 Train Loss: 1.1693 Accuracy: 0.9439 Time: 7.87143  | Val Loss: 1.2481 Accuracy: 0.9256\n",
      "Epoch: 99/150 Train Loss: 1.1622 Accuracy: 0.9484 Time: 7.62362  | Val Loss: 1.2451 Accuracy: 0.9264\n",
      "Epoch: 100/150 Train Loss: 1.1597 Accuracy: 0.9496 Time: 7.66224  | Val Loss: 1.2517 Accuracy: 0.9220\n",
      "Epoch: 101/150 Train Loss: 1.1548 Accuracy: 0.9487 Time: 7.66265  | Val Loss: 1.2461 Accuracy: 0.9261\n",
      "Epoch: 102/150 Train Loss: 1.1429 Accuracy: 0.9538 Time: 7.63338  | Val Loss: 1.2444 Accuracy: 0.9297\n",
      "Epoch: 103/150 Train Loss: 1.1546 Accuracy: 0.9516 Time: 7.59452  | Val Loss: 1.2462 Accuracy: 0.9276\n",
      "Epoch: 104/150 Train Loss: 1.1319 Accuracy: 0.9602 Time: 7.61494  | Val Loss: 1.2522 Accuracy: 0.9254\n",
      "Epoch: 105/150 Train Loss: 1.1468 Accuracy: 0.9524 Time: 7.65689  | Val Loss: 1.2549 Accuracy: 0.9233\n",
      "Epoch: 106/150 Train Loss: 1.1419 Accuracy: 0.9579 Time: 7.64414  | Val Loss: 1.2387 Accuracy: 0.9274\n",
      "Epoch: 107/150 Train Loss: 1.1373 Accuracy: 0.9573 Time: 7.60322  | Val Loss: 1.2446 Accuracy: 0.9299\n",
      "Epoch: 108/150 Train Loss: 1.1479 Accuracy: 0.9523 Time: 7.64654  | Val Loss: 1.2518 Accuracy: 0.9248\n",
      "Epoch: 109/150 Train Loss: 1.1305 Accuracy: 0.9604 Time: 7.83972  | Val Loss: 1.2453 Accuracy: 0.9284\n",
      "Epoch: 110/150 Train Loss: 1.1236 Accuracy: 0.9642 Time: 7.59661  | Val Loss: 1.2497 Accuracy: 0.9294\n",
      "Epoch: 111/150 Train Loss: 1.1290 Accuracy: 0.9610 Time: 7.61071  | Val Loss: 1.2427 Accuracy: 0.9299\n",
      "Epoch: 112/150 Train Loss: 1.1282 Accuracy: 0.9609 Time: 7.76103  | Val Loss: 1.2419 Accuracy: 0.9297\n",
      "Epoch: 113/150 Train Loss: 1.1262 Accuracy: 0.9606 Time: 7.84745  | Val Loss: 1.2414 Accuracy: 0.9304\n",
      "Epoch: 114/150 Train Loss: 1.1265 Accuracy: 0.9605 Time: 7.62341  | Val Loss: 1.2407 Accuracy: 0.9294\n",
      "Epoch: 115/150 Train Loss: 1.1223 Accuracy: 0.9627 Time: 7.74642  | Val Loss: 1.2459 Accuracy: 0.9297\n",
      "Epoch: 116/150 Train Loss: 1.1140 Accuracy: 0.9647 Time: 7.66174  | Val Loss: 1.2430 Accuracy: 0.9294\n",
      "Epoch: 117/150 Train Loss: 1.1183 Accuracy: 0.9637 Time: 7.67086  | Val Loss: 1.2443 Accuracy: 0.9276\n",
      "Epoch: 118/150 Train Loss: 1.1157 Accuracy: 0.9651 Time: 7.86567  | Val Loss: 1.2447 Accuracy: 0.9274\n",
      "Epoch: 119/150 Train Loss: 1.1131 Accuracy: 0.9669 Time: 7.76042  | Val Loss: 1.2540 Accuracy: 0.9276\n",
      "Epoch: 120/150 Train Loss: 1.1126 Accuracy: 0.9675 Time: 7.63016  | Val Loss: 1.2498 Accuracy: 0.9279\n",
      "Epoch: 121/150 Train Loss: 1.1153 Accuracy: 0.9668 Time: 7.77853  | Val Loss: 1.2501 Accuracy: 0.9297\n",
      "Epoch: 122/150 Train Loss: 1.1136 Accuracy: 0.9673 Time: 8.04943  | Val Loss: 1.2384 Accuracy: 0.9315\n",
      "Epoch: 123/150 Train Loss: 1.1131 Accuracy: 0.9672 Time: 7.59181  | Val Loss: 1.2429 Accuracy: 0.9282\n",
      "Epoch: 124/150 Train Loss: 1.1173 Accuracy: 0.9658 Time: 7.62905  | Val Loss: 1.2377 Accuracy: 0.9287\n",
      "Epoch: 125/150 Train Loss: 1.1074 Accuracy: 0.9676 Time: 7.91079  | Val Loss: 1.2409 Accuracy: 0.9269\n",
      "Epoch: 126/150 Train Loss: 1.1031 Accuracy: 0.9704 Time: 7.70617  | Val Loss: 1.2482 Accuracy: 0.9256\n",
      "Epoch: 127/150 Train Loss: 1.1068 Accuracy: 0.9677 Time: 7.90256  | Val Loss: 1.2395 Accuracy: 0.9292\n",
      "Epoch: 128/150 Train Loss: 1.1111 Accuracy: 0.9667 Time: 7.64540  | Val Loss: 1.2524 Accuracy: 0.9292\n",
      "Epoch: 129/150 Train Loss: 1.1023 Accuracy: 0.9698 Time: 7.63016  | Val Loss: 1.2463 Accuracy: 0.9287\n",
      "Epoch: 130/150 Train Loss: 1.1033 Accuracy: 0.9697 Time: 7.59945  | Val Loss: 1.2436 Accuracy: 0.9289\n",
      "Epoch: 131/150 Train Loss: 1.1024 Accuracy: 0.9712 Time: 7.59883  | Val Loss: 1.2413 Accuracy: 0.9299\n",
      "Epoch: 132/150 Train Loss: 1.1004 Accuracy: 0.9705 Time: 7.54825  | Val Loss: 1.2393 Accuracy: 0.9269\n",
      "Epoch: 133/150 Train Loss: 1.1060 Accuracy: 0.9682 Time: 7.66838  | Val Loss: 1.2398 Accuracy: 0.9312\n",
      "Epoch: 134/150 Train Loss: 1.1101 Accuracy: 0.9678 Time: 7.68399  | Val Loss: 1.2432 Accuracy: 0.9289\n",
      "Epoch: 135/150 Train Loss: 1.1016 Accuracy: 0.9725 Time: 7.67573  | Val Loss: 1.2366 Accuracy: 0.9312\n",
      "Epoch: 136/150 Train Loss: 1.0952 Accuracy: 0.9737 Time: 7.76618  | Val Loss: 1.2395 Accuracy: 0.9297\n",
      "Epoch: 137/150 Train Loss: 1.0944 Accuracy: 0.9744 Time: 7.70291  | Val Loss: 1.2393 Accuracy: 0.9315\n",
      "Epoch: 138/150 Train Loss: 1.1036 Accuracy: 0.9710 Time: 7.89631  | Val Loss: 1.2403 Accuracy: 0.9302\n",
      "Epoch: 139/150 Train Loss: 1.1022 Accuracy: 0.9686 Time: 7.61976  | Val Loss: 1.2427 Accuracy: 0.9304\n",
      "Epoch: 140/150 Train Loss: 1.0946 Accuracy: 0.9717 Time: 7.66393  | Val Loss: 1.2423 Accuracy: 0.9330\n",
      "Epoch: 141/150 Train Loss: 1.0908 Accuracy: 0.9733 Time: 7.52495  | Val Loss: 1.2373 Accuracy: 0.9310\n",
      "Epoch: 142/150 Train Loss: 1.0998 Accuracy: 0.9694 Time: 7.82114  | Val Loss: 1.2406 Accuracy: 0.9317\n",
      "Epoch: 143/150 Train Loss: 1.0966 Accuracy: 0.9707 Time: 7.63223  | Val Loss: 1.2389 Accuracy: 0.9325\n",
      "Epoch: 144/150 Train Loss: 1.0938 Accuracy: 0.9736 Time: 7.65322  | Val Loss: 1.2384 Accuracy: 0.9327\n",
      "Epoch: 145/150 Train Loss: 1.1002 Accuracy: 0.9719 Time: 7.68455  | Val Loss: 1.2370 Accuracy: 0.9320\n",
      "Epoch: 146/150 Train Loss: 1.0913 Accuracy: 0.9756 Time: 7.68777  | Val Loss: 1.2367 Accuracy: 0.9307\n",
      "Epoch: 147/150 Train Loss: 1.0955 Accuracy: 0.9714 Time: 7.63074  | Val Loss: 1.2376 Accuracy: 0.9315\n",
      "Epoch: 148/150 Train Loss: 1.0943 Accuracy: 0.9734 Time: 7.80653  | Val Loss: 1.2357 Accuracy: 0.9315\n",
      "Epoch: 149/150 Train Loss: 1.1026 Accuracy: 0.9706 Time: 7.68999  | Val Loss: 1.2351 Accuracy: 0.9325\n",
      "Epoch: 150/150 Train Loss: 1.0947 Accuracy: 0.9726 Time: 7.77964  | Val Loss: 1.2338 Accuracy: 0.9345\n"
     ]
    }
   ],
   "source": [
    "from hdd.data_util.auto_augmentation import ImageNetPolicy\n",
    "\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "from hdd.models.cnn.regnet import create_regnet\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    net,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    lr=1e-3,\n",
    "    weight_decay=1e-3,\n",
    "    max_epochs=100,\n",
    ") -> dict[str, list[float]]:\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, max_epochs, eta_min=lr / 100\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return training_stats\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader\n",
    "\n",
    "\n",
    "# 我们提前计算好了训练数据集上的均值和方差\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "\n",
    "stats = {}\n",
    "net_names = [\"regnet_y_400mf\", \"regnet_y_800mf\"]\n",
    "batch_sizes = [64, 32]\n",
    "for net_name, batch_size in zip(net_names, batch_sizes):\n",
    "    net = create_regnet(net_name)\n",
    "    net = net.to(DEVICE)\n",
    "    train_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            RandomResize([256, 288, 320, 352]),\n",
    "            transforms.RandomCrop(224),\n",
    "            transforms.RandomHorizontalFlip(),\n",
    "            ImageNetPolicy(),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    train_dataset.transform = train_dataset_transforms\n",
    "    val_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(256),\n",
    "            transforms.CenterCrop(224),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    val_dataset.transform = val_dataset_transforms\n",
    "    train_dataloader, val_dataloader = build_dataloader(\n",
    "        batch_size, train_dataset, val_dataset\n",
    "    )\n",
    "    print(f\"-----------------------{net_name}------------------------\")\n",
    "    max_epochs = 150\n",
    "    stats[net_name] = train_net(\n",
    "        net,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        lr=0.001,\n",
    "        weight_decay=0,\n",
    "        max_epochs=max_epochs,\n",
    "    )\n",
    "    del net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "628a2677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = stats[net_names[0]].keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(stats[net_names[0]][field], label=net_names[0], linestyle=\"--\")\n",
    "    plt.plot(stats[net_names[1]][field], label=net_names[1])\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "599b9486",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
